Description
Species-800 is a corpus for species entities, which is based on manually annotated abstracts. It comprises 800 PubMed abstracts that contain identified organism mentions.
This model is trained with the BertForTokenClassification method from the transformers library and imported into Spark NLP.
Predicted Entities
SPECIES
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")\
sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
ner_model = MedicalBertForTokenClassifier.pretrained("bert_token_classifier_ner_species", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("ner")\
.setCaseSensitive(True)\
.setMaxSentenceLength(512)
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[
document_assembler,
sentence_detector,
tokenizer,
ner_model,
ner_converter
])
data = spark.createDataFrame([["""As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) ."""]]).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val ner_model = MedicalBertForTokenClassifier.pretrained("bert_token_classifier_ner_species", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("ner")
.setCaseSensitive(True)
.setMaxSentenceLength(512)
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(document_assembler,
sentence_detector,
tokenizer,
ner_model,
ner_converter))
val data = Seq("""As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.token_bert.species").predict("""As determined by 16S rRNA gene sequence analysis, strain 6C (T) represents a distinct species belonging to the class Betaproteobacteria and is most closely related to Thiomonas intermedia DSM 18155 (T) and Thiomonas perometabolis DSM 18570 (T) .""")
Results
+-----------------------+-------+
|ner_chunk |label |
+-----------------------+-------+
|6C (T) |SPECIES|
|Betaproteobacteria |SPECIES|
|Thiomonas intermedia |SPECIES|
|DSM 18155 (T) |SPECIES|
|Thiomonas perometabolis|SPECIES|
|DSM 18570 (T) |SPECIES|
+-----------------------+-------+
Model Information
| Model Name: | bert_token_classifier_ner_species |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 404.2 MB |
| Case sensitive: | true |
| Max sentence length: | 512 |
References
https://species.jensenlab.org/
Benchmarking
label precision recall f1-score support
B-SPECIES 0.6073 0.9374 0.7371 767
I-SPECIES 0.7418 0.8648 0.7986 1043
micro-avg 0.6754 0.8956 0.7701 1810
macro-avg 0.6745 0.9011 0.7678 1810
weighted-avg 0.6848 0.8956 0.7725 1810